Wildlife Communication

This report contains a progress report for the ph.d. project titled “Wildlife Communication”. The project focuses on investigating how signal processing and pattern recognition can be used to improve wildlife management in agriculture. Wildlife management systems used today experience habituation from wild animals which makes them ineffective. An intelligent wildlife management system could monitor its own effectiveness and alter its scaring strategy based on this

Lead concentrations in blood from incubating common eiders (Somateria mollissima) in the Baltic Sea

Here we investigate if lead may be a contributing factor to the observed population decline in a Baltic colony of incubating eiders (Somateria mollissima). Body mass and blood samples were obtained from 50 incubating female eiders at the Baltic breeding colony on Christiansø during spring 2017 (n = 27) and 2018 (n = 23). All the females were sampled twice during early (day 4) and late (day 24) incubation. The full blood was analysed for lead to investigate if the concentrations exceeded toxic thresholds or changed over the incubation period due to remobilisation from bones and liver tissue. Body mass, hatch date and number of chicks were also analysed with respect to lead concentrations. The body mass (mean ± SD g) increased significantly in the order: day 24 in 2018 (1561 ± 154 g) Peer reviewe

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system

Automatic Detection of Animals in Mowing Operations Using Thermal Cameras

During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge <em>(Perdix perdix)</em> or pheasant <em>(Phasianus colchicus)</em> are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare <em>(Lepus europaeus)</em> and fawns of roe deer <em>(Capreolus capreolus)</em> to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken <em>(Gallus domesticus)</em> and a rabbit <em>(Oryctolagus cuniculus)</em> in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future